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“A filmmaker should never assume he’s superior to his subject. I often find that even the simplest topic remains an enigma. The best film portraits not only evoke that enigma but ingest it in a process that renders what’s invisible visible.” – Damian Pettigrew, a documentary SME

The same statement above holds for database SMEs using the new fast ingest process. Because their data might be at one point neither visible nor invisible. From DBA point of view I mean…

Memoptimized Rowstore fast ingest is a new feature of Oracle 19c and is all about high performance data streaming. Here is an example from Oracle which is ultra fast, arguably 25 million inserts per second on two socket server:

Memoptimized Rowstore was introduced in Oracle 18c and here are few good blog posts on the subject:

The fast ingest functionality of Memoptimized Rowstore enables fast data inserts into an Oracle Database from applications that ingest small but high volume transactions with almost no transactional overhead. The inserted data is temporarily buffered in the large pool before writing it to disk in bulk in a deferred, asynchronous manner.

Memoptimized Rowstore provides the following two functionalities:

Fast ingest:
– Fast ingest optimizes the processing of high-frequency, single-row data inserts into a database
– Fast ingest uses the large pool for buffering the inserts before writing them to disk, so as to improve data insert performance

Fast lookup:
– Fast lookup enables fast retrieval of data from for high-frequency queries
– Fast lookup uses a separate memory area in the SGA called the memoptimize pool for buffering the data queried from tables
– For using fast lookup, you must allocate appropriate memory size to the memoptimize pool using MEMOPTIMIZE_POOL_SIZE

The memoptimize pool contains two parts: (1) the memoptimize buffer area which occupies 75% of the memoptimize pool and (2) a hash index that is a non-persistent segment data structure having several units containing a number of hash buckets. The hash index occupies 25% of the memoptimize pool.

Note that a table can be configured for using both fast ingest and fast lookup but these 4 points should be always considered:

– Because fast ingest buffers data in the large pool, there is a possibility of data loss in the event of a system failure. To avoid data loss, a client must keep a local copy of the data after performing inserts, so that it can replay the inserts in the event of a system failure before the data is written to disk. A client can use the DBMS_MEMOPTIMIZE package subprograms to track the durability of the inserts. After inserts are written to disk, a client can destroy its local copy of the inserted data.

Use the GET_APPLY_HWM_SEQID function to find the low high-water mark of sequence numbers of the records that have been successfully written to disk across all the sessions:

select DBMS_MEMOPTIMIZE.GET_APPLY_HWM_SEQID from dual;

Equivalently, use the GET_WRITE_HWM_SEQID function to return the high-water mark sequence number of the record that has been written to the large pool for the current session.

select DBMS_MEMOPTIMIZE.GET_WRITE_HWM_SEQID from dual;

– Queries do not read data from the large pool, hence data inserted using fast ingest cannot be queried until it is written to disk.

If you need to flush all the fast ingest data from the large pool to disk for the current session, just run:

exec DBMS_MEMOPTIMIZE.WRITE_END;

– Parent-child transactions must be synchronized to avoid errors. For example, foreign key inserts and updates of rows inserted into the large pool can return errors, if the parent data is not yet written to disk.

– Index operations are supported by fast ingest similar to the regular inserts. However, for fast ingest, database performs index operations while writing data to disk, and not while writing data into the large pool.

You can disable a table for fast ingest by specifying the NO MEMOPTIMIZE FOR WRITE clause in the ALTER TABLE statement. You can view the fast ingest data in the large pool using the V$MEMOPTIMIZE_WRITE_AREA view. You can also view and control the fast ingest data in the large pool using the subprograms of the packages DBMS_MEMOPTIMIZE and DBMS_MEMOPTIMIZE_ADMIN.

The DBMS_MEMOPTIMIZE_ADMIN package has only one procedure WRITES_FLUSH which does not accept any input or output parameters.

exec DBMS_MEMOPTIMIZE.WRITES_FLUSH;

Here is one more example from Oracle:

As you see, you can lose data using this new technique. Use it with caution unless you can afford that or re-insert the data.

If you need to remove or populate fast lookup data for a certain table from the memoptimize pool, use DBMS_MEMOPTIMIZE.DROP_OBJECT and DBMS_MEMOPTIMIZE.POPULATE respectively.

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Julian is the Global Database Lead of Accenture. His primary responsibility is managing and leading the Global Oracle Technology Practice which includes Autonomous Cloud, IaaS, PaaS, Database Services, Engineered Systems, Java, Middleware, Security and all other areas falling under Oracle Technology. He is also the Accenture-Enkitec Group Managing Director for ... Continue reading →